Application of machine learning to stochastic effect analysis of chemically amplified resists used for extreme ultraviolet lithography

Abstract

Chemically amplified resists will be used in the high numerical aperture (NA) tools of extreme ultraviolet lithography. However, stochastic defects are a serious problem for their application to the high NA tools. In this study, the stochastic defect generation was simulated on the basis of the sensitization mechanisms and analyzed to clarify the contribution of process and material parameters using machine learning. The half-pitch HP, the sensitivity s, the total sensitizer concentration C s, the effective reaction radius for deprotection R eff, and the initial standard deviation of the number of protected units per polymer molecule σ i were used as variables. As a result, the exponential function reproduced the simulation results well. s and HP had dominant effects in LER formation. For pinching, s and HP were dominant. σ i had a major effect. For bridging, s and HP were also dominant, the effect of σ i was not major and C s and R eff effects were major.

General-Purpose Algorithm for Two-Material Minimum Feature Size Enforcement of Freeform Nanophotonic Devices

“…The first step to correcting this problem is to identify the specific pixels which are oscillating. The existence of nonconvergent pixels can be identified by comparing masks across iterations: (11) When this condition is true, but the MFS constraints (eq 8) are not satisfied, then the algorithm has begun to oscillate. It is possible to extract the problematic pixels F by applying an XOR between the mask after it has undergone an open and after a close: (12) where T = A i−1 ○ B f .…”

“…Additionally, other recent work has provided analyses of the effects of specific fabrication defects on device performance. These include using 3D modeling of line-edge roughness, 10 estimating the stochastic behavior of lithographic resists using deep learning, 11 determining the performance consequences of photonic waveguide couplers under common fabrication defects 12 and detecting fill MFS violations via an analysis of freeform vector graphic masks. 13 These can be used a posteriori to analyze chosen designs or during an optimization process itself as a way of filtering out sensitive designs and MFS violations.…”

Estimation of effective reaction radius for catalytic chain reaction of chemically amplified resist by Bayesian optimization

“…Previously, investigations of stochastic defects suggested that LER can be suppressed by increasing the effective reaction radius for deprotection (R p ), 2,9) which is an essential parameter indicating the efficiency of chemical reactions per unit diffusion length of acids. Thus, R p is related to both sensitivity and resolution.…”

Bayesian optimization-based estimation of effective reaction radius of chemically amplified resist in acid catalyzed deprotection reaction

“…One of the critical parameters in suppress LER investigated in the previous study was the effective reaction radius for deprotection reaction (Rp). 8 Rp indicate the efficiency of chemical reactions per unit diffusion length of acids. However, Rp cannot be directly measured by experiments.…”